Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

Monitoring and modelling of chlorophyll-a concentrations in rivers using a high-resolution satellite image: a case study in the Nakdong river,Korea

The feasibility of using remote-sensing data with high spatial resolution was assessed for monitoring and modelling of chlorophyll-a (chl-a) in river waters. Two-band and three-band reflectance models including the red-edge band were examined as spectral coefficients using a RapidEye image for river waters, where the scale is smaller and narrower than for ocean waters. A red?red-edge?NIR three-band model calculated by a cubic function explained 73% of variance in the estimated data using the relationship between spectral indices such as absorption coefficients obtained using the model and chl-a concentrations and performed better than the red?red-edge two-band. Chl-a concentrations were simulated by a one-dimensional water quality model, QUALKO2, and image-derived and measured chl-a concentrations were applied in the calibration step of simulation. The image-derived chl-a dataset showed more stable calibration throughout the study area and enhanced the results rather than measured data. It is expected that chl-a estimation techniques using high resolution satellite data, RapidEye, have the capability to support rapid and widespread water quality monitoring and modelling, when a field dataset is not large or precise enough to do it, but still requires the improvement of estimation accuracy.     

Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas,China

In optically complex waters, it is important to evaluate the accuracy of the standard satellite chlorophyll-a (chl-a) concentration algorithms, and to develop accurate algorithms for monitoring the dynamics of chl-a concentration. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing reflectance and concurrent in situ measured chl-a (2010–2013) were used to evaluate the standard OC3M algorithm (ocean chlorophyll-a three-band algorithm for MODIS) and Graver–Siegel–Maritorena model version 1 (GSM01) algorithm for estimating chl-a concentration in the Bohai and Yellow Seas (BYS). The results showed that the chl-a algorithms of OC3M and GSM01 with global default parameters presented poor performance in the BYS (the mean absolute percentage difference (MAPD) and coefficient of determination (R²) of OC3M are 222.27% and 0.25, respectively; the MAPD and R² of GSM01 are 118.08% and 0.07, respectively). A novel statistical algorithm based on the generalized additive model (GAM) was developed, with the aim of improving the satellite-derived chl-a accuracy. The GAM algorithm was established using the in situ measured chl-a concentration as the output variable, and the MODIS above water remote-sensing reflectance (visible bands at 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm) and bathymetry (water depth) as input variables. The MAPD and R² calculated between the GAM and the in situ chl-a concentration are 39.96% and 0.67, respectively. The results suggest that the GAM algorithm can yield a superior performance in deriving chl-a concentrations relative to the standard OC3M and GSM01 algorithms in the BYS.     

Evaluation of atmospheric correction and high-resolution processing on SeaDAS-derived chlorophyll-a: an example from mid-latitude mesotrophic waters

Over the last 15 years, great effort has gone into the development of chlorophyll-a (chl-a) retrieval algorithms for case 2 waters, where variations in the water leaving radiance signal are not well correlated with concentrations of chl-a. In this study, we investigate the effectiveness of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived chl-a retrieval algorithms in the less productive coastal waters around Tasmania, Australia. Algorithms were evaluated using matches between satellite imagery and in-situ water samples (number of samples, n = 16–65) derived from a 604 sample data set collected over a 9-year period. Three aerosol correction models and three chl-a retrieval algorithms were evaluated using both standard and high-resolution processing procedures using the National Aeronatics and Space Adminstration’s SeaDAS software package. chl-a retrievals were evaluated in Bass Strait, where in-situ chl-a was less than 1 mg m^?3 and retrievals were less affected by coloured dissolved organic matter. chlor_a, the default SeaDAS chl-a product, with the Management unit of the North Sea Mathematical models aerosol correction algorithm performed best (root mean square error (RMSE) = 0.09 mg m^?3; mean absolute percentage error (MAPE) = 34%; coefficient of determination, R² = 0.75). The fluorescence line height algorithm using Rayleigh corrected top of atmosphere reflectances (RMSE = 0.11 mg m^?3, MAPE = 41%, R² = 0.61) may provide an alternative in waters where full atmospheric correction is problematic and the two-band red/near-infrared algorithm failed to provide a meaningful estimate of chl-a. High-resolution processing of MODIS imagery improved spatial resolution but reduced chl-a retrieval accuracy, reducing the agreement between measured and predicted levels by between 12% and 25% depending on the retrieval algorithm. The SeaDAS default chlor_a product proved superior to the alternatives in mid-latitude mesotrophic coastal waters with low chl-a concentrations. In addition, there appears little benefit in using MODIS high-resolution processing mode for chl-a retrievals.     

Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study

Accurate remote assessment of phytoplankton chlorophyll a (chla) concentration is particularly challenging in turbid, productive waters. Recently a conceptual model containing reflectance in three spectral bands in the red and near infra-red range of the spectrum was suggested for retrieving chla concentrations in turbid productive waters; it was calibrated and validated in lakes and reservoirs in Nebraska and Iowa. The objective of this paper is to evaluate the performance of this three band model as well as its special case, the two-band model to estimate chla concentration in Chesapeake Bay, as representative of estuarine Case II waters, and to assess the accuracy of chla retrieval. To evaluate the model performance, dual spectroradiometers were used to measure subsurface spectral radiance reflectance in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained widely variable chla (9 to 77.4 mg/m³) and total suspended solids (7-65 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 440 nm was 0.20 to 2.50 m^− 1; Secchi disk transparency ranged from 0.28 to 1.5 m. The two- and three-band models were spectrally tuned to select the spectral bands for most accurate chla estimation. Strong linear relationships were established between analytically measured chla and both the three-band model [R^− 1(675)-R^− 1(695)] × R(730) and the two-band model R(720)/R(670), where R(λ) is reflectance at wavelength λ. The three-band model accounted for 81% of variation in chla and allowed estimation of chla with a root mean square error (RMSE) of less than 7.9 mg/m³, whereas the two-band model accounted for 79% of chla variability and RMSE of chla estimation was below 8.4 mg/m³. The three-band model with MERIS spectral bands allows accurate chla estimation with RMSE below 9.1 mg/m³. Two-band model with SeaWiFS bands and MODIS 667 nm and 748 nm bands can estimate chla with RMSE below 11 mg/m³. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of this algorithm for chla retrieval in turbid, productive estuarine waters.     

Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices

Foliar pigment concentrations of chlorophylls and cartenoids are important indicators of plant physiological status, photosynthesis rate, and net primary productivity. Although the utility of hyperspectral derived vegetation indices for estimating foliar pigment concentration has been documented for many vegetation types, floating macrophytes have not been assessed despite their ecological importance. This study surveyed 39 wetland species (12 floating macrophytes (FM), 8 grasses/sedges/rushes (GSR), and 19 herbs/wildflowers (HWF)) to determine whether foliar pigment concentrations could be estimated from hyperspectral reflectance. Hyperspectral reflectance of samples was recorded using an ASD FieldSpec3 Max portable spectroradiometer with the plant probe attachment or via a typical laboratory set-up. A semi-empirical relationship was established using either a linear, second-degree polynomial or logarithmic function between 13 candidate vegetation indices and chl-a, chl-b, Car, and chl-a + b pigment concentrations. Vegetation indices R-M, CI-Red, and MTCI were strongly correlated with foliar pigment concentrations using a linear fitting function. Chl-a + b and chl-b concentrations for all samples were reasonably estimated by the R-M index (R² = 0.66 and 0.64), although Chl-a and Car concentration estimates using CI-Red were weaker (R² = 0.63 and 0.51). Regression results indicate that pooled samples to estimate individual foliar pigments were less correlated than when each type of vegetation type was treated separately. For instance, chl-a + b was best estimated by CI-Red for FM (R² = 0.80), MTCI for HWF (R² = 0.77), and R-M for GSR (R² = 0.67). Although floating macrophytes feature unique adaptions to their aquatic environment, their foliar pigment concentrations and spectral signatures were comparable to other wetland vegetation types. Overall, vegetation indices that exploit the red-edge region were a reasonable compromise, having good explanatory power for estimation of foliar pigments across the sampled wetland vegetation types and with CI-Red the best suited index for floating macrophytes.     

Optical properties of inorganic suspended solids and their influence on ocean colour remote sensing in highly turbid coastal waters

The influence of the optical properties of inorganic suspended solids (ISS) on in-water algorithms was evaluated using an optical model in highly turbid coastal water, whose ISS concentration reached several hundred grams per cubic metre. The measurements were conducted in the upper Gulf of Thailand. The backscattering coefficient of the ISS was calculated using the Lorenz–Mie scattering theory. On the basis of the measurement, the ISS size distribution was parameterized as a function of ISS concentration, and both the spherical and non-spherical particle shape models were evaluated. For ISS concentrations of 10 g m^?3, an estimate of the chlorophyll-a (chl-a) concentration within a factor of 2 on a logarithmic scale is possible in a [chl-a] range of 4–30 mg m^?3. The differential coefficient of remote sensing reflectance was calculated to evaluate its respective sensitivities for chl-a and ISS concentrations. The use of radiometric data at 670 nm (700–900 nm) is valid for in-water algorithms used to estimate chl-a (ISS) concentration in highly turbid coastal waters.     

Retrieval of leaf chlorophyll content in field crops using narrow-band indices: effects of leaf area index and leaf mean tilt angle

Reliable estimation of leaf chlorophyll-a and -b content (chl-a + b) at canopy scales is essential for monitoring vegetation productivity, physiological stress, and nutrient availability. To achieve this, narrow-band vegetation indices (VIs) derived from imaging spectroscopy data are commonly used. However, VIs are affected by canopy structures other than chl-a + b, such as leaf area index (LAI) and leaf mean tilt angle (MTA). In this study, we evaluated the performance of 58 VIs reported in the literature to be chl-a + b-sensitive against a unique measured set of species-specific leaf angles for six crop species in southern Finland. We created a large simulated canopy reflectance database (100,000 canopy configurations) using the physically based PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models) model. The performance of model-simulated indices was compared against airborne AISA Eagle II imaging spectroradiometer data and field-measured chl-a + b, LAI, and MTA values. In general, LAI had a positive effect on the strength of the correlation between chl-a + b and VIs while MTA had a negative effect in both measured and simulated data. Three indices (REIP (red edge inflection point), TCARI (transformed chlorophyll absorption ratio index)/OSAVI (optimized soil-adjusted vegetation index), and CTR6 (Carter indices)) showed strong correlations with chl-a + b and similar performance in model-simulated and measured data set. However, only two (TCARI/OSAVI and CTR6) were independent from LAI and MTA. We consider these two indices robust proxies of crop leaf chl-a + b.     

Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay: a comparison of remote sensing algorithms

Ocean colour imagery is used increasingly as a tool to assess water quality via chlorophyll-a concentration (chl-a) estimations in European waters. The Bay of Biscay is affected by major river discharges, which alter the constituents of the marine waters. Chlorophyll-a algorithms, designed for use at global scales, are less accurate due to the variability of optically active in-water constituents. Hence, regionally parameterized empirical algorithms are necessary. The main objective of the present study was to develop a regional algorithm to retrieve chl-a in surface water using in situ R _rs, for a subsequent application to Medium Resolution Imaging Spectrometer (MERIS) satellite images. To address this objective, a platform was developed initially and a measurement procedure adapted for the field HR4000CG Spectrometer. Subsequently, the procedure was tested during a survey over the south-eastern Bay of Biscay (North-East Atlantic Ocean), to establish a MERIS chl-a algorithm for the area, by comparing different global remote sensing chl-a algorithms, with band ratios. Results validated with the jackknife resampling procedure show a satisfactory relationship between the R _rs(510)/R _{r s}(560) and chl-a (R ² _jac?=?0.681). This ratio is better correlated to chl-a than those obtained with established chl-a remote sensing algorithms. High content in coloured dissolved organic matter (CDOM > 0.4 m^?1) and suspended particulate matter (SPM > 2.8 mg l^?1) influenced this relationship, with yellow substances having a stronger effect.     

Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River,India

ABSTRACT

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.     

MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009

The Medium Resolution Imaging Spectrometer (MERIS) was used to investigate the spatial and temporal dynamics of chlorophyll-a (chl-a) in Erhai Lake, the second largest freshwater lake in the Yunnan province of China. Six chl-a retrieval models, including four Basic ERS & Envisat (A)ATSR and Meris Toolbox (BEAM) software-incorporated algorithms and MERIS three-band and two-band models, were validated to find the best fit to extract chl-a concentration in Erhai Lake. With a chl-a range of 5–15 mg m^–3, the Lakes/Eutrophic method showed the best performance. The algorithm was then applied to eight-year cloud-free MERIS images between 2003 and 2009, with seasonal and inter-annual variability analysed. Long-term chl-a distributions of Erhai Lake revealed significant seasonal and inter-annual variability. The mean chl-a of the south lake was higher in summer (14.3 mg m^–3) than in spring (10.1 mg m^–3), while generally lower chl-a was found in the north lake with a mean chl-a of 6.4 mg m^–3 in spring and 9.0 mg m^–3 in summer, respectively. An increasing trend was found between 2006 and 2009, and the increasing rate was 12.9% for annual chl-a of the entire lake. While chl-a seasonality was attributed to the seasonal changes of the local temperature, the inter-annual variation was possibly linked to the discharged wastewater from Dali City. This work could provide critical information for decision-makers to manage Erhai Lake’s aquatic ecosystems.     

Using hyperspectral data to quantify water-quality parameters in the Wabash River and its tributaries,Indiana

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R²) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R² = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.     

Time-series analysis of chlorophyll-a,sea surface temperature,and sea surface height anomalies during 2003–2014 with special reference to El Niño,La Niña,and Indian Ocean Dipole (IOD) Years

The present study was carried out to find the variability of chlorophyll-a (chl-a) concentration, sea surface temperature (SST), and sea surface height anomalies (SSHa) during 2003–2014, covering the Bay of Bengal (BoB) and Arabian Sea (AS) waters. These parameters were linked with El Niño, La Niña, and Indian Ocean Dipole (IOD) years. The observed results during 2003–2014 were evaluated and it was found that the monthly mean value for 12-year data ranged as follows: chl-a (0.11–0.46 mg m^?3), SST (27–31 °C), and SSHa (?0.2 to 20 cm). The annual mean range of chl-a for 12-year data was 0.1–0.23 mg m^?3, the SST range was 27–28 °C, and the SSHa range was 2.14–13.91 cm. It has been observed that with the SST range of 27–28 °C and the SSHa range of 7–9 cm, the chl-a concentration enhanced to 0.20–0.23 mg m^?3. With a higher SST range of 28–29 °C and with a positive SSHa range of 11–14 cm, the chl-a concentration appeared to be low (0.17–0.18 mgm^?3). During normal years, SSHa was positive with the >5 to <10 cm range during the months of April–June, which coincided with an increase in SST, >2 to <4 °C. During the normal years, SSHa (>?0.2 to a concentration (>0.3 to <0.5 mg m^?3) was noticed during December–February in the BoB and AS. Compared to the BoB chl-a range (<0.4 mg m^?3), a high chl-a concentration was observed in AS (>0.4 mg m^?3). However, during the phenomenon years, the study area had experienced low chl-a (<0.2 mg m^?3), high SST (>5 °C), and more positive SSHa (>10 to <20 cm) during January–March and October–December in AS and BoB. The present study infers that a positive IOD leads to low chl-a concentration (<2 mg m^?3) and low primary productivity in AS. El Niño caused the down-welling process, it results in a low chl-a concentration (<1 mg m^?3) in BoB and AS. La Niña caused the upwelling process, and it results in a high chl-a concentration (>2.0 mg m^?3) in BoB and AS. In the recent past years (2003–2014), the intensity and frequency of El Niño, La Niña, and IOD have been increasing, evidenced with few studies, and have impacts on the Indian Ocean climate. Therefore, the influences of the relative changes of these phenomena on the BoB and AS need to be understood for productivity assessment and ocean state monitoring.     

Spatio-temporal distribution of chlorophyll-a concentration derived from MODIS satellite remote-sensing and in situ observations in Sanya Bay,China

This study examined satellite chlorophyll-a (chl-a) concentration and in situ observations in Sanya Bay (SYB). In situ observation of chl-a was conducted four times per year at 12 sampling stations in SYB from January 2004 to October 2008. Monthly satellite chl-a was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during 2000–2012. This study compared satellite chl-a values to in situ measurements in SYB. The two data sets match well in the whole region except for two estuaries. Results show that the average in situ chl-a was 1.49 mg m^?3 in SYB. Chl-a was relatively higher (>2 mg m^?3) and more variable in coastal areas, with a tendency to decrease offshore (<0.4 mg m^?3). The chl-a level in summer displayed obviously vertical stratification, with higher values at the bottom and lower values at the surface. Analysis of monthly mean chl-a showed that the highest level (>2 mg m^?3) appeared in December, with the lowest in March (<1 mg m^?3). The gradients are ranked winter, autumn, summer and spring. There was higher chl-a in autumn and winter, which may be associated with the stronger wind monsoon then. Annual mean chl-a from 2000 to 2012 varied from 1.17 to 2.05 mg m^?3, with the minimum in 2001 and the maximum in 2005. The chl-a level presented a roughly increasing tendency from 2000 to 2012, which may be related to the increasing nutrients associated with the development of tourism and fishery.     

Spatio-temporal remote sensing of vertical and seasonal chlorophyll-a:b ratio in Tamarix ramosissima

Remotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R² > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRI_mod, R₅₁₀–R₅₇₀ nm) performed better than the original PRI (PRI, R₅₃₁–R₅₇₀ nm) with R² improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRI_mod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.     

The effect of phytoplankton pigment composition and packaging on the retrieval of chlorophyll-a concentration from satellite observations in the Southern Ocean

The Antarctic waters are known to be optically unique and the standard empirical ocean colour algorithms applied to these waters may not address the regional bio-optical characteristics. This article sheds light on the performance of current empirical algorithms and a regionally optimized algorithm (ROA) for the retrieval of chlorophyll-a (chl-a) concentration from Aqua-Moderate Resolution Imaging Spectroradiometer (Aqua-MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in the Indian Ocean Sector of Southern Ocean (IOSO). Analysis indicated that empirical algorithms used for the retrieval of chl-a concentration from Aqua-MODIS and SeaWiFS underestimate by a factor varying from 2 to 2.9, resulting in underestimation when in situ chl-a exceeds about 0.3 mg m^?3. To explain these uncertainties, a study was carried out to understand the effect of phytoplankton pigment composition and pigment packaging on remote-sensing reflectance (R_rs,λ), from the analysis of phytoplankton-specific absorption coefficient (a_ph,*_λ). The spatial variation of phytoplankton groups analysed using diagnostics pigments (DP) indicated shifting of the phytoplankton community structure from offshore to coastal Antarctic, with a significant increasing trend for diatoms and a decreasing trend for haptophytes population. The diatom-dominated population exhibits lower a_ph,*_λ in the 405–510 nm region (with relative flattening in 443–489 nm) compared with the a_ph,*_λ spectra of the haptophytes-dominated population that peaks near 443 nm. The flattening of a_ph,*_λ spectra for the diatom-dominated population was attributed to its larger cell size, which leads to pigment packaging (intracellular shading) and in turn results in higher R_rs,λ. The relationship between pigment composition (normalized by chl-a) and blue:green absorption band ratios (a_ph,*₄₄₃:a_ph,*₅₅₅ and a_ph,*₄₈₉:a_ph,*₅₅₅) corresponding to the Aqua-MODIS and SeaWiFS bands showed in-phase associations with most of the pigments such as 19?-hexanoyloxyfucoxanthin, 19?-butanoyloxyfucoxanthin, peridinin, and zeaxanthin. In contrast, the out-of-phase association observed between the blue:green absorption ratios and fucoxanthin indicated apparent deviations from the general pigment retrieval algorithms, which assumes that blue:green ratios vary in a systematic form with chl-a. The out-of-phase correspondence suggests that the increasing trend of fucoxanthin pigments towards the Antarctic coast was associated with the decreasing trend of blue:green absorption ratios and in turn results in higher R_rs,λ. Therefore, an increase in R_rs,λ leads to underestimation of chl-a from Aqua-MODIS and SeaWiFS in the IOSO region.     

Effect of optically active substances and atmospheric correction schemes on remote-sensing reflectance at a coastal site off Kochi

The present study focused on understanding the variability of optically active substances (OASs) and their effect on spectral remote-sensing reflectance (R_rs). Furthermore, the effect of atmospheric correction schemes on the retrieval of chlorophyll-a (chl-a) from satellite data was also analysed. The OASs considered here are chl-a, coloured dissolved organic matter (CDOM), and total suspended matter (TSM). Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite was used for this study. The two atmospheric correction schemes considered were: multi-scattering with two-band model selection NIR correction (hereon referred as ‘A1’) and Management Unit of the North Sea Mathematical Models (MUMM) correction and MUMM NIR calculation (hereafter referred as ‘A2’). The default MODIS bio-optical algorithm (OC3M) was used for the retrieval of chl-a. Analysis of OASs showed that chl-a was the major light-absorbing component, with highly variable distribution (0.006–25.85 mg m^–3). Absorption due to CDOM at 440 nm (a_CDOM440) varied from 0.002 to 0.31 m^–1 whereas TSM varied from 0.005 to 33.44 mg l^–1. The highest concentration of chl-a was observed from August to November (i.e. end of the southwest monsoon and beginning of the northeast monsoon), which was attributed to coastal upwelling. The average value of a_CDOM440 was found to be lower than the global mean. A significant negative relationship between a_CDOM440 and salinity during the southwest monsoon indicated that much of the CDOM during this season was derived from river discharge. Spectral R_rs was found to be strongly linked to the variability in chl-a concentration, indicating that chl-a was the major light-absorbing component. Satellite-derived spectral R_rs was in good agreement with that in situ when chl-a concentration was lower than 5 mg m^–3. The validation of chl-a, derived from in situ R_rs, showed moderate performance (correlation coefficient, R² = 0.64; log₁₀(RMSE) = 0.434; absolute percentage difference (APD) = 43.6% and relative percentage difference (RPD) = 42.33%). However the accuracy of the algorithm was still within acceptable limits. The statistical analysis for atmospheric correction schemes showed improved mean ratio of measured to estimated chl-a (‘r’ = 1.6), log₁₀(RMSE) (0.49), APD (25.46%), and RPD (17.57%) in the case of A1 as compared with A2, whereas in the case of A2, R² (0.56), slope (0.26), and intercept (0.27) were better as compared with A1. The two atmospheric correction schemes did not show any significant statistical difference. However the default atmospheric correction scheme (A1) was found to be performing comparatively better probably due to the fact that the concentration of TSM and CDOM was much lower to overcome the impact of chl-a.     

Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements

Landsat TM data and field spectral measurements were used to evaluate chlorophyll‐a (Chl‐a) concentration levels and trophic states for three inland lakes in Northeast China. Chl‐a levels were estimated applying regression analysis in the study. The results obtained from the field reflectance spectra indicate that the ratio between the reflectance peak at 700 nm and the reflectance minimum at 670 nm provides a relatively stable correlation with Chl‐a concentration. Their determination of coefficients R ² is 0.69 for three lakes in the area. From Landsat TM data, the results show that the most successful Chl‐a was estimated from TM3/TM2 with R ² = 0.63 for the two lakes on 26 July 2004, from TM4/TM3 with R ² = 0.89 for the two lakes on 14 October 2004, and from the average of TM2, TM3 and TM4 with R ² = 0.72 for the three lakes tested on 13 July 2005. These results are applicable to estimate Chl‐a from satellite‐based observations in the area. We also evaluate the trophic states of the three lakes in the region by employing Shu's modified trophic state index (TSI_M) for the Chinese lakes' eutrophication assessment. Our study presents the TSI_M from different TM data with R ² more than 0.73. The study shows that satellite observations are effectively applied to estimate Chl‐a levels and trophic states for inland lakes in the area.     

A nitrogen spectral response model and nitrogen estimation of summer maize during the entire growth period

ABSTRACT

Timely and effective prediction of nitrogen content in summer maize could provide support data for precise fertilization. In this study, the feasibility and expansibility of predicting the nitrogen mechanism model of summer maize leaves through its entire growth period were investigated on the basis of the theory of leaf radiation transmission mechanism. A complete random test of data from two maize varieties and two nitrogen fertilizer applications in 2017 was conducted. Three versions of the leaf optical PROperties SPECTra (PROSPECT) model, namely, PROSPECT-4, PROSPECT-5, and PROSPECT-D were used to link the established leaf nitrogen density (LND) and chlorophyll-a + b (chl-a + b) models, that is, chl-a + b-LND model. A nitrogen response transfer model (N-RTM) was established by linking the optimal PROSPECT and chl-a + b-LND models. Results were as follows. (1) chl-a + b estimation using the PROSPECT-D model yielded the highest accuracy (the coefficient of determination (R²) = 0.774, the normalized root mean squared error (nRMSE) = 13.19%) among the three PROSPECT models, it shows that the model considering more factors can better reflect the internal law of blade, and could be used as the basic model of N-RTM; (2) Established chl-a + b-LND models based on the dataset from each growth stage showed differences using the confidence interval method, and the R² values of the optimal regression model at V12, VT, and R3 were 0.794, 0.781, and 0.821, respectively. Based on the changes of chl-a + b and LND during the growth period, a piecewise model was constructed; (3) The R² and nRMSE values between the measured and estimated LNDs were 0.656% and 22.86%, respectively. The validation results are better than the traditional empirical model. The results showed that the segmented model, which considered the interaction of various factors within the leaves and the change of chl-a + b-LND during the growth period, had better performance in nitrogen monitoring. The constructed nitrogen model in this study preliminarily realized the remote sensing prediction of the nitrogen mechanism model and had a certain mechanism.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.